As Social Computing has increasingly captivated the general public, it has become a popular research area for computer scientists. Social Computing research focuses on online social behavior and using artifacts derived from it for providing recommendations and other useful community knowledge. Unfortunately, some of that behavior and knowledge incur societal costs, particularly with regards to Privacy, which is viewed quite differently by different populations as well as regulated differently in different locales. But clever technical solutions to those challenges may impose additional societal costs, e.g., by consuming substantial resources at odds with Green Computing, another major area of societal concern. We propose a new crosscutting research area, Societal Computing, that focuses on the technical tradeoffs among computational models and application domains that raise significant societal issues. We highlight some of the relevant research topics and open problems that we foresee in Societal Computing. We feel that these topics, and Societal Computing in general, need to gain prominence as they will provide useful avenues of research leading to increasing benefits for society as a whole. This thesis will consist of the following four projects that aim to address the issues of Societal Computing.
First, privacy in the context of ubiquitous social computing systems has become a major concern for society at large. As the number of online social computing systems that collect user data grows, concerns with privacy are further exacerbated. Examples of such online systems include social networks, recommender systems, and so on. Approaches to addressing these privacy concerns typically require substantial extra computational resources, which might be beneficial where privacy is concerned, but may have significant negative impact with respect to Green Computing and sustainability, another major societal concern. Spending more computation time results in spending more energy and other resources that make the software system less sustainable. Ideally, what we would like are techniques for designing software systems that address these privacy concerns but which are also sustainable — systems where privacy could be achieved “for free,” i.e., without having to spend extra computational effort. We describe how privacy can indeed be achieved for free — an accidental and beneficial side effect of doing some existing computation — in web applications and online systems that have access to user data. We show the feasibility, sustainability, and utility of our approach and what types of privacy threats it can mitigate.
Second, we aim to understand what the expectations and needs to end-users and software developers are, with respect to privacy in social systems. Some questions that we want to answer are: Do end-users care about privacy? What aspects of privacy are the most important to end-users? Do we need different privacy mechanisms for technical vs. non-technical users? Should we customize privacy settings and systems based on the geographic location of the users? We have created a large scale user study using an online questionnaire to gather privacy requirements from a variety of stakeholders. We also plan to conduct follow-up semistructured interviews. This user study will help us answer these questions.
Third, a related challenge to above, is to make privacy more understandable in complex systems that may have a variety of user interface options, which may change often. Our approach is to use crowdsourcing to find out how other users deal with privacy and what settings are commonly used to give users feedback on aspects like how public/private their settings are, what common settings are typically used by others, where do a certain users’ settings differ from a trusted group of friends, etc. We have a large dataset of privacy settings for over 500 users on Facebook and we plan to create a user study that will use the data to make privacy settings more understandable.
Finally, end-users of such systems find it increasingly hard to understand complex privacy settings. As software evolves over time, this might introduce bugs that breach users’ privacy. Further, there might be system-wide policy changes that could change users’ settings to be more or less private than before. We present a novel technique that can be used by end-users for detecting changes in privacy, i.e., regression testing for privacy. Using a social approach for detecting privacy bugs, we present two prototype tools. Our evaluation shows the feasibility and utility of our approach for detecting privacy bugs. We highlight two interesting case studies on the bugs that were discovered using our tools. To the best of our knowledge, this is the first technique that leverages regression testing for detecting privacy bugs from an end-user perspective.
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More About This Work
- Academic Units
- Computer Science
- Department of Computer Science, Columbia University
- Columbia University Computer Science Technical Reports, CUCS-026-13
- Published Here
- October 29, 2014